po-vincent / jarvis

JARVIS, a system to connect LLMs with ML community

JARVIS

This project is under construction and we will have all the code ready soon.

Updates

• [2023.4.1] We update a version of code for building.

Overview

Language serves as an interface for LLMs to connect numerous AI models for solving complicated AI tasks!

See our paper: HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

We introduce a collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors (from HuggingFace Hub). The workflow of our system consists of four stages:

• Task Planning: Using ChatGPT to analyze the requests of users to understand their intention, and disassemble them into possible solvable sub-tasks.
• Model Selection: Based on the sub-tasks, ChatGPT invoke the corresponding models hosted on HuggingFace.
• Task Execution: Executing each invoked model and returning the results to ChatGPT.
• Response Generation: Finally, using ChatGPT to integrate the prediction of all models, and generate response.

System Requirements

• Ubuntu 20.04 LTS
• NVIDIA GeForce RTX 3090 * 1
• RAM >= 80GB

Quick Start

First replace openai.key and huggingface.cookie in server/config.yaml with your personal key and your cookies at huggingface.co. Then run the following commands:

For server:

# setup env
cd server
conda create -n jarvis python=3.8
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

# download models
cd models
sh download.sh

# run server
cd ..
python models_server.py
python bot_server.py --config config.yaml # for text-davinci-003

For web:

cd web
npm install
npm run dev

Note that in order to display the video properly in HTML, you need to compile ffmpeg manually with H.264

# This command need be executed without errors.
LD_LIBRARY_PATH=/usr/local/lib /usr/local/bin/ffmpeg -i input.mp4 -vcodec libx264 output.mp4

Screenshots

Citation

If you find this work useful in your method, you can cite the paper as below:

@article{shen2023hugginggpt,
    title={HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace},
    author={Shen, Yongliang and Song, Kaitao and Tan, Xu and Li, Dongsheng and Lu, Weiming and Zhuang, Yueting},
    journal={arXiv preprint arXiv:2303.17580},
    year={2023}
}

Acknowledgement

• ChatGPT
• HuggingFace
• ControlNet
• ChatGPT-vue